Steady-State Versus Chemotherapy-Based Stem Cell Mobilization in Multiple Myeloma: A Single-Center Study to Analyze Efficacy and Safety
Bibliographic record
Abstract
Background: High-dose chemotherapy followed by autologous hematopoietic stem cell support is recommended in the treatment of eligible multiple myeloma (MM) patients. The aim of this study was to compare the efficacy and safety of steady-state versus chemotherapy-based stem cell mobilization in our Hungarian patient population. Methods: The subjects were 210 MM patients who underwent stem cell mobilization procedure between 2018 and 2022. Solo granulocyte colony-stimulating factor (G-CSF) was administered in 104 cases, while 106 patients received chemotherapy which was followed by G-CSF administration. We evaluated the ratio of successful mobilizations, the amount of collected stem cells, the incidence of infections and cost-effectivity in the two groups. Results: In the steady-state group, there was a significantly higher need for plerixafor (45% vs. 13%, P < 0.001), unsuccessful stem cell mobilization was more frequent (11% vs. 3%, P = 0.024) and the mean amount of collected stem cells was lower (6.9 vs. 9.8 × 10 6 , P < 0.001) than in the chemotherapy group. However, infections were less frequent (4% vs. 27%, P < 0.001) and the number of days spent in hospital was significantly lower (6 vs. 14 days, P < 0.001). Plerixafor was more frequently administered in those who had received lenalidomide or daratumumab than in those who had been treated with other regimens (41% vs. 23%, P = 0.007 and 78% vs. 23%, P < 0.001, respectively). Conclusions: Steady-state mobilization is a safe method; however, the higher rate of plerixafor administration and unsuccessful attempts may question its superiority to chemomobilization. J Hematol. 2024;13(3):79-85 doi: https://doi.org/10.14740/jh1256
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".